Determining regional-scale carbon losses from tropical peatlands using ISBAS-InSAR.

80% of peatland in Indonesia and Malaysia (15% of Earth's soil carbon) is now drained for production of pulpwood and palm oil. Associated increases in peat decomposition and large-scale forest fires are now significant contributors to global greenhouse gas emissions. However, carbon losses from these processes and the impact of peatland drainage remain poorly quantified across SE Asia because of the challenging scale and inaccessibility of dense tropical peatland forests.

Field-based subsidence measurements are therefore a spatially limited approach for assessing carbon losses from tropical peatlands. Space-based platforms offer the opportunity for regular and efficient pan-regional monitoring. A development in satellite interferometric synthetic aperture radar (InSAR), a technique that measures surface motion, has the potential to solve this problem. A new 'intermittent small baseline subset' (ISBAS) modelling technique, developed at the University of Nottingham, provides excellent coverage across almost all land surfaces irrespective of ground cover. This enables long term measurement of peatland surface motion across whole catchments, regions and countries. Importantly, the ISBAS technique is able to determine surface deformation under tropical forest canopy using C-band InSAR data, enabling continuous monitoring of surface motion ranging from 0.1 - 40 cm/yr at spatial scales 90x90m. Thus, space-based InSAR-derived motions should directly relate to tropical peatland carbon loss, allowing pan-regional assessment of carbon loss from otherwise inaccessible tropical peatlands. This is a novel approach as InSAR is yet to have been used to determine subsidence of tropical peatland sites.

This project aims to determine whether rate of subsidence of the peat surface measured by InSAR is a proxy for rate of carbon loss in tropical peatlands in SE Asia. InSAR and field measurements of subsidence will be combined with peat carbon stock data using statistical regression modelling techniques to validate the use of subsidence as a proxy for carbon loss.